/* * public RinexNavigationParserGps(EphemerisResponse ephResponse) * { * foreach (GnssEphemeris eph in ephResponse.ephList) * { * if (eph is GpsEphemeris) * { * this.eph.Add(new EphGps((GpsEphemeris)eph)); * } * } * this.iono = new IonoGps(ephResponse.ionoProto); * } */ public SatellitePosition getGpsSatPosition(Observations obs, int satID, char satType, double receiverClockError) { long unixTime = obs.getRefTime().getMsec(); double range = obs.getSatByIDType(satID, satType).getPseudorange(0); if (range == 0) { return(null); } EphGps eph = findEph(unixTime, satID, satType); if (eph.Equals(EphGps.UnhealthyEph)) { return(SatellitePosition.UnhealthySat); } if (eph != null) { // char satType = eph.getSatType(); SatellitePosition sp = computePositionGps(obs, satID, satType, eph, receiverClockError); // SatellitePosition sp = computePositionGps(unixTime, satType, satID, eph, range, receiverClockError); //if(receiverPosition!=null) earthRotationCorrection(receiverPosition, sp); return(sp);// new SatellitePosition(eph, unixTime, satID, range); } return(null); }
void EnvironmentStep() { if (stepCount % DecisionPeriod == 0) { var actionReq = new BrainActionRequest(); foreach (KeyValuePair <int, RemoteAction> agent in m_RemoteAgents) { float[] lowerObs = agent.Value.remoteAgent.GetLowerObservations(); float[] upperObs = agent.Value.remoteAgent.GetUpperObservations(); var obs = new Observations() { }; obs.LowerObservations.AddRange(lowerObs); obs.UpperObservations.AddRange(upperObs); obs.ArucoMarkerID = agent.Value.remoteAgent.m_ArucoMarkerID; actionReq.Observations.Add(obs); } // Send sensor data to remote brain brainActionRes = brainServerClient.GetAction(actionReq); MakeActions(brainActionRes); } else if (TakeActionsBetweenDecisions) { MakeActions(brainActionRes); } stepCount++; }
void run_mean_bench() { var cycles = Pow2.T12; var samples = Pow2.T14; var src = Numeric.force <long, double>(Random.Span <long>(samples, Interval.closed(-2000L, 2000L))); var ds = Observations.Load(src); var dst = 0.0; var last = 0.0; var sw1 = stopwatch(); for (var i = 0; i < cycles; i++) { last = ds.Mean(ref dst); } sw1.Stop(); var sw2 = stopwatch(); for (var i = 0; i < cycles; i++) { last = src.Avg(); } sw2.Stop(); ReportBenchmark("mkl-ssmean", cycles * samples, sw1.Elapsed); ReportBenchmark("direct", cycles * samples, sw2.Elapsed); }
public async Task <IActionResult> Edit(string id, [Bind("ApplicationUserID,CategorieId,dateObservation,rating,Nom,text,typeSuivie")] Observations observations) { if (id != observations.ApplicationUserID) { return(NotFound()); } if (ModelState.IsValid) { try { _context.Update(observations); await _context.SaveChangesAsync(); } catch (DbUpdateConcurrencyException) { if (!ObservationsExists(observations.ApplicationUserID)) { return(NotFound()); } else { throw; } } return(RedirectToAction(nameof(Index))); } ViewData["CategorieId"] = new SelectList(_context.Categories, "Id", "Id", observations.CategorieId); ViewData["ApplicationUserID"] = new SelectList(_context.Users, "Id", "Id", observations.ApplicationUserID); return(View(observations)); }
static unsafe Observations <T> ApplyRadixSort <T>(this Observations <T> samples, Observations <T> dst) where T : unmanaged { var dim = samples.Dim; var sampleCount = samples.Count; var iStorage = (int)VslSSMatrixStorage.VSL_SS_MATRIX_STORAGE_ROWS; var mformat = VslSSMatrixStorage.VSL_SS_MATRIX_STORAGE_ROWS; var taskPtr = IntPtr.Zero; if (typeof(T) == typeof(float)) { VSL.vslsSSNewTask(ref taskPtr, ref dim, ref sampleCount, ref mformat, ref MemoryMarshal.Cast <T, float>(samples)[0]).AutoThrow(); } else if (typeof(T) == typeof(double)) { VSL.vsldSSNewTask(ref taskPtr, ref dim, ref sampleCount, ref mformat, ref MemoryMarshal.Cast <T, double>(samples)[0]).AutoThrow(); } else { throw Unsupported.define <T>(); } using var handle = VslSSTaskHandle.Wrap <T>(taskPtr); handle.Set(VSL_SS_ED_OBSERV_STORAGE, ref iStorage); handle.Set(VSL_SS_ED_SORTED_OBSERV, ref dst[0]); handle.Set(VSL_SS_ED_SORTED_OBSERV_STORAGE, ref iStorage); handle.Compute(VSL_SS_SORTED_OBSERV, VSL_SS_METHOD_RADIX); return(dst); }
static unsafe ref T CalcMean <T>(this Observations <T> samples, ref T dst) where T : unmanaged { using var h2 = VslSSTaskHandle.Create(samples); h2.Set(VSL_SS_ED_MEAN, ref dst); h2.Compute(Calcs.VSL_SS_MEAN, VSL_SS_METHOD_FAST); return(ref dst); }
static unsafe Observations <T> CalcVariance <T>(this Observations <T> samples, Observations <T> dst) where T : unmanaged { using var h2 = VslSSTaskHandle.Create(samples); h2.Set(VSL_SS_ED_2R_MOM, ref dst[0]); h2.Compute(Calcs.VSL_SS_2R_MOM, VSL_SS_METHOD_FAST); return(dst); }
public void sumvals() { var src = Random.Stream <double>().Take(16000).ToArray(); var expect = src.Sum().Round(4); var actual = Observations.Load(src).Sum()[0].Round(4); Claim.require(gmath.within(expect, actual, .01)); }
private void SetObservations() { var o = new Observation { Id = 1, StartObservationDate = DateTime.Parse("12.03.2014"), EndObservationDate = DateTime.Parse("12.03.2015"), PatientId = 2, DiagnosisId = 3, DoctorId = 2 }; Observations.Add(o); o = new Observation { Id = 2, StartObservationDate = DateTime.Parse("22.09.2016"), EndObservationDate = DateTime.Parse("12.10.2016"), PatientId = 1, DiagnosisId = 1, DoctorId = 3 }; Observations.Add(o); o = new Observation { Id = 4, StartObservationDate = DateTime.Parse("01.01.2017"), EndObservationDate = DateTime.Parse("13.01.2017"), PatientId = 1, DiagnosisId = 1, DoctorId = 1 }; Observations.Add(o); o = new Observation { Id = 5, StartObservationDate = DateTime.Parse("25.09.2018"), EndObservationDate = DateTime.Parse("07.10.2018"), PatientId = 1, DiagnosisId = 3, DoctorId = 2 }; Observations.Add(o); o = new Observation { Id = 3, StartObservationDate = DateTime.Parse("10.10.2007"), EndObservationDate = DateTime.Parse("12.10.2017"), PatientId = 3, DiagnosisId = 3, DoctorId = 1 }; Observations.Add(o); }
static unsafe Observations <T> CalcExtrema <T>(this Observations <T> samples, Observations <T> dst) where T : unmanaged { using var h2 = VslSSTaskHandle.Create(samples); h2.Set(VSL_SS_ED_MIN, ref dst[0]); h2.Set(VSL_SS_ED_MAX, ref dst[1]); h2.Compute(Calcs.VSL_SS_MAX | Calcs.VSL_SS_MIN, VSL_SS_METHOD_FAST); return(dst); }
public void mean() { var src = Random.Span <long>(Pow2.T14, Interval.closed(-2000L, 2000L)); var expect = gAlg.avg(src); var converted = Numeric.force <long, double>(src); var actual = (long)Observations.Load(converted).Mean()[0]; Claim.eq(expect, actual); }
public void FilterAndJoin() { //filter subjects by arms if (Arms.Any()) { Subjects = Subjects.FindAll(s => Arms.Select(a => a.Id).Contains(s.StudyArmId)).ToList(); } Debug.WriteLine(Subjects.Count, " AFTER ARMS"); //filter subjects by studies if (Studies.Any()) { Subjects = Subjects.FindAll(subj => Studies.Select(st => st.Id).Contains(subj.StudyId)).ToList(); } Debug.WriteLine(Subjects.Count, " AFTER Studies"); //filter subjects by subCharacteristics if (SubjChars.Any()) { Subjects = Subjects.FindAll(s => SubjChars.Select(sc => sc.SubjectId).Contains(s.Id)).ToList(); } Debug.WriteLine(Subjects.Count, " AFTER SubjChars"); //filter by visits //TODO //TODO : WILL RETRIEVE SUBJECTS THAT HAVE SAME UNIQUE IDS ACROSS PROJECTS (i.e. need to load observations to Mongo with //TODO: DB subjectId //filter observations for filtered subjects Observations = Observations?.FindAll(o => Subjects.Select(s => s.UniqueSubjectId).Contains(o.USubjId)); //filter subjects by selected observations if (Observations.Any() && ObservationsFiltered) { Subjects = Subjects.FindAll(s => Observations.Select(o => o.USubjId).Contains(s.UniqueSubjectId)); } Debug.WriteLine(Subjects.Count, " AFTER syncing with observations"); //FILTER SAMPLES BY SELECTED AND FILTERED SAMPLE CHARACTERISTICS if (SampleCharacteristics.Any()) { Samples = Samples.FindAll(s => SampleCharacteristics.Select(sc => sc.SampleId).Contains(s.Id)).ToList(); } //TODO: TEMP FILTERING BY COLLECTION STUDY DAY //SYNCHRONIZE SAMPLES AND SUBJECTS if (Samples.Any()) { Samples = Samples.FindAll(s => Subjects.Select(sc => sc.Id).Contains(s.SubjectId)).ToList(); Subjects = Subjects.FindAll(sb => Samples.Select(sp => sp.SubjectId).Contains(sb.Id)).ToList(); } }
public ActionResult Create([Bind(Include = "Observation_Types,time_stamp,value")] Observations observations) { if (ModelState.IsValid) { db.Observations.Add(observations); db.SaveChanges(); return(RedirectToAction("Index")); } return(View(observations)); }
static public Tuple <double, double> ComputeParas( IEnumerable <double> samples ) { var meanVar = Observations.GetMeanVar(samples); var mean = meanVar.Item1; var var = meanVar.Item2; var p = ((1 - mean) / var - 1 / mean) / Math.Pow(mean, 2); var q = p * (1 / mean - 1); return(new Tuple <double, double>(p, q)); }
public void minval() { var samplesize = Pow2.T14; var s1Range = Interval.closed(350.0, 1000.0); var s1 = Random.Array <double>(samplesize, s1Range); var s1Max = Observations.Load(s1).Max()[0]; NumericClaims.neq(s1Max, 0.0); var zeroCount = s1.Count(x => x == 0); Notify($"Found {zeroCount} zeroes"); }
public override void OnEpisodeBegin() { if (firstDecision) { firstDecision = false; return; } Debug.Log("episode begin number: " + episodeCount); this.latestObservations = new Observations(1, 1, 1, 1, 1, 1, new float[4], new float[4]); episodeCount++; initGame(); // Find the ball and Reset the position of the ball and paddle and time SummonPlayer(); // Change the player stopped = false; }
public async void GetObservationsAsync() { if (monitoringPressure) { var retrievedObservations = await observationRepository.GetByPatientAndBloodPressure(PatientId); foreach (var o in retrievedObservations) { if (Observations.Where(observation => observation.Id == o.Id).ToList().Count() == 0) { Observations.Add(o); } } } if (monitoringCholesterol) { var retrievedObservations = await observationRepository.GetByPatientAndTotalCholesterol(PatientId); foreach (var o in retrievedObservations) { if (Observations.Where(observation => observation.Id == o.Id).ToList().Count() == 0) { Observations.Add(o); } } } if (monitoringTobacco) { var retrievedObservations = await observationRepository.GetByPatientAndTobacco(PatientId); foreach (var o in retrievedObservations) { if (Observations.Where(observation => observation.Id == o.Id).ToList().Count() == 0) { Observations.Add(o); } } } foreach (var o in Observations) { foreach (var observer in Observers) { observer.OnNext(o); } } }
public bool deleteObservation(long id) { MySql.Data.MySqlClient.MySqlConnection conn; string myConnectionString = ConfigurationManager.ConnectionStrings["AzureDB"].ConnectionString; conn = new MySql.Data.MySqlClient.MySqlConnection(); try { conn.ConnectionString = myConnectionString; conn.Open(); Observations o = new Observations(); MySql.Data.MySqlClient.MySqlDataReader mySQLReader = null; // Get The Observation with that ID (For Deleting) String sqlString = "SELECT * FROM observations WHERE id = " + id.ToString(); MySql.Data.MySqlClient.MySqlCommand cmd = new MySql.Data.MySqlClient.MySqlCommand(sqlString, conn); mySQLReader = cmd.ExecuteReader(); if (mySQLReader.Read()) { mySQLReader.Close(); // Delete the Observation with the id that was found sqlString = "DELETE FROM observations WHERE id = " + id.ToString(); cmd = new MySql.Data.MySqlClient.MySqlCommand(sqlString, conn); cmd.ExecuteNonQuery(); return(true); } else { return(false); } } catch (MySql.Data.MySqlClient.MySqlException ex) { throw ex; } finally { conn.Close(); } }
public override async Task InitializeAsync(object navigationData) { if (Observations == null) { Observations = new ObservableCollection <Observation>(); } IEnumerable <Observation> observations = await _observationsService.GetObservationsAsync(); Observations.Clear(); foreach (Observation observation in observations) { Observations.Add(observation); } await base.InitializeAsync(navigationData); }
public SatellitePosition getGpsSatPosition(Observations obs, int satID, char satType, double receiverClockError) { RinexNavigationParserGalileo rnp = getRNPByTimestamp(unixTime, initialLocation); if (rnp != null) { if (rnp.isTimestampInEpocsRange(unixTime)) { return(rnp.getGalileoSatPosition(unixTime, range, satID, satType, receiverClockError)); } else { return(null); } } return(null); }
public void radixSort() { var obs = Pow2.T10; var dim = Pow2.T08; var range = Interval.closed(-20f, 20f); var src = Random.Array <float>(dim * obs, range); var sample = Observations.Load(src, dim); var sorted = sample.RadixSort(); for (var i = 0; i < obs; i++) { var v = sorted.Observation(i); for (var j = 0; j < dim - 1; j++) { ClaimNumeric.lteq(v[j], v[j + 1]); } } }
public void CreateMt2203Generators() { var gencount = Pow2.T08; var samplesize = Pow2.T16; var seeds = Random.Array <uint>(gencount); var streams = new MklRng[gencount]; for (var i = 0; i < gencount; i++) { streams[i] = rng.mt2203(seeds[i], i); } var bufferF64 = new double[samplesize]; var bufferU32 = new uint[samplesize]; var bufferI32 = new int[samplesize]; var ufRange = Interval.closed(1.0, 250.0); for (var i = 0; i < gencount; i++) { var stream = streams[i]; sample.uniform(stream, ufRange, bufferF64); Observations.Load(bufferF64, 1).Extrema(); var max = Observations.Load(bufferF64, 1).Max()[0]; NumericClaims.lteq(max, ufRange.Right); NumericClaims.neq(max, 0); sample.bits(stream, bufferU32); sample.bernoulli(stream, .40, bufferI32); for (var j = 0; j < samplesize; j++) { Claim.require(bufferI32[j] == 0 || bufferI32[j] == 1); } sample.gaussian(stream, .75, .75, bufferF64); sample.laplace(stream, .5, .5, bufferF64); } for (var i = 0; i < gencount; i++) { streams[i].Dispose(); } }
public override void Initialize() { string strFilePath = @"./data.csv"; File.WriteAllText(strFilePath, "session id;brick height;paddle speed;ball speed;paddle length;ball size;time;paddle distance;ballHits;ballBounces;amount of bricks;win/lose;type of personality;playerAPM;playerReactionTime;playerPaddleSafety;GEQ - content;GEQ - skillful;GEQ - occupied;GEQ - difficulty;satisfaction"); //COMMENT THIS IF YOU JUST WANT TO APPEND - last 5 are player attributes File.AppendAllText(strFilePath, Environment.NewLine); round = 0; generatePlayerList(); this.latestObservations = new Observations(1, 1, 1, 1, 1, 1, new float[4], new float[4]); paddle = GameObject.Find("Paddle"); ball = GameObject.Find("Ball"); stopped = true; requestingDecision = false; haveParameters = false; RequestDecision(); Time.timeScale = 10.0f; }
public void Add(PROBLEM input, SOLUTION output) { foreach (var observer in Observers) { var observations = new List <Observation>(); //observer.GetObservations(fact, observations); Observations.AddRange(observations); } var constraints = new List <Constraint <SOLUTION> >(); // Use observations to form conclusions foreach (var observation in Observations) { } Constraints = constraints; }
public override void CollectObservations() { // Target and Agent positions Observations obs = manager.getObservations(); //AddVectorObs(obs.position_head); //AddVectorObs(obs.position_food); AddVectorObs(direction); AddVectorObs(Vector2.Distance(obs.position_food, obs.position_head)); //foreach (Vector2 v in obs.position_tail) { // AddVectorObs(v); //} //foreach (Vector2 v in obs.position_walls) { // AddVectorObs(v); //} //foreach (Vector2 v in obs.position_empty) { // AddVectorObs(v); //} }
public Tracker(Patient patient, string type) { this.Type = type; this.Patient = patient; this.Observations = patient.GetAllObservationsByCodeText(type); Observations.Sort((x, y) => { if (x.Issued == null || y.Issued == null) { return(1); } return(((DateTime)x.Issued).CompareTo((DateTime)y.Issued)); }); this.GraphData = new List <decimal>(); foreach (Observation observation in Observations) { GraphData.Add((decimal)observation.MeasurementResult.Value); } }
/// <summary> /// /// </summary> /// <param name="samples"></param> /// <returns></returns> /// <remarks> /// if X~BetaSpanned(min,max,p,q) then /// Y=(X-min)/(max-min) ~ BetaSpanned(0,1,p,q)=Beta(p,q) /// accourding to std Beta, /// E(Y)= p/(p+q), D(Y)=a * b / ((a + b) * (a + b) * (a + b + 1)) /// So /// (E(X)-min)/(max-min)=p/(p+q), so E(x)=p/(p+q)*span+min /// so E(x)= (aq+bp) / (p+q) /// D(Y)=D(X)/span^2, so D(X)=D(Y)*span^2 /// /// </remarks> static public Tuple <double, double> ComputeParas( IEnumerable <double> samples ) { var min = samples.Min(); var max = samples.Max(); var span = max - min; var meanVar = Observations.GetMeanVar(samples); var mean = meanVar.Item1; var var = meanVar.Item2; var lambda = (mean - min) * (max - mean) / var - 1; var p = lambda * (mean - min) / span; var q = lambda * (max - min) / span; return(new Tuple <double, double>(p, q)); }
private void CreateObsMethod() { if (Prescriptions == null || Prescriptions.Equals("") || BloodPressure.Equals("0") || Weight.Equals("0")) { MaterialMessageBox.ShowError("Remplir les champs obligatoire de l'observation (poids, pression sanguine, prescription"); return; } Model.Observation obs = new Model.Observation() { bloodPressure = _bloodPressure, comment = Comment, date = Date, pictures = Pictures.ToArray(), prescription = Prescriptions.Split('\n'), weight = _weight }; int patientId = lastWindow.SelectedPatient.id; Observations.CreateObservation(patientId, obs); lastWindow.SelectedPatient.observations.Add(obs); lastWindow.SelectedObservation = obs; CloseWindow(); }
public async Task <IActionResult> Create([Bind("ApplicationUserID,CategorieId,rating,Nom,text,typeSuivie")] Observations observations) { if (ModelState.IsValid) { // enregister les info dans categories var userId = _userManager.GetUserId(HttpContext.User); observations.ApplicationUserID = userId; observations.dateObservation = DateTime.Now; _context.Add(observations); await _context.SaveChangesAsync(); var listCat = await _context.Observations.ToListAsync(); return(RedirectToAction("Create", new { IsSuccess = true })); } ViewData["CategorieId"] = new SelectList(_context.Categories, "Id", "NomCategorie", observations.CategorieId); ViewData["ApplicationUserID"] = new SelectList(_context.Users, "Id", "UserName", observations.ApplicationUserID); return(View(observations)); }
public override void AddObservations() { Observations.Add(Energy, "Energy"); }